Study information

AI and Data Science Methods for Life and Health Sciences - 2024 entry

MODULE TITLEAI and Data Science Methods for Life and Health Sciences CREDIT VALUE15
MODULE CODEMTHM015 MODULE CONVENERProf Kirsty Wan (Coordinator)
DURATION: TERM 1 2 3
DURATION: WEEKS 11
Number of Students Taking Module (anticipated) 20
DESCRIPTION - summary of the module content
 
Analysing data and quantitatively comparing mathematical models to data are crucial when using mathematics to improve our understanding of complex biological systems. Data from biology experiments and clinical recordings are diverse and often present challenges for analysis and modelling, such as high dimensionality and non-stationarity. This module will introduce you to some common kinds of data observed in biological and clinical applications such as images, time series and high dimensional sequencing data. You will be introduced to advanced methods that deal with these data, but can also be applicable in other fields, for example in climate systems and finance. 
 
Competence in a scientific programming language (such as Matlab or Python) is highly desirable
AIMS - intentions of the module
 
We will introduce data and methods that arise in the above application areas but are also applicable in other fields. The content will be centred on real-world applications: for example, the analysis of motility in single cell organisms, analysis of clinical time series in neurology and neuroendocrinology as well as analysis of next generation sequencing (NGS) data. The study of these examples will require theory in:
image analysis: feature extraction/identification, object tracking, deep learning; time series analysis: clustering, linear and nonlinear time series analysis methods, calibration of models from dynamic data; NGS data: enrichment analysis and dimension reduction (e.g. PCA, UMAP, t-SNE). The presentation of theory will be given in the context of practical, hands-on analysis of real-world data. 
 
 
INTENDED LEARNING OUTCOMES (ILOs) (see assessment section below for how ILOs will be assessed)

On successful completion of this module you should be able to:

Module Specific Skills and Knowledge

1. Apply common tools for image and time series analysis
2. Apply common algorithms for object detection
3. Develop a working knowledge of supervised and unsupervised learning
4. Study common tools for dimension reduction
5. Learn how to link mechanistic models and data.

Discipline Specific Skills and Knowledge

6. Develop practical skills in handling different kinds of data
7. Develop practical skills to link models and data

Personal and Key Transferable / Employment Skills and Knowledge

9. Develop practical skills in handling diverse data
10. Build the ability to identify which techniques are suitable for which problems
11. Communicate ideas effectively by interpreting real data

 

SYLLABUS PLAN - summary of the structure and academic content of the module
 
(i) Image analysis
  • Introduction to the problem of detecting and tracking objects in images (application to microswimmers and medical MRI)
  • Deep learning for object detection
(ii) Time series analysis
  • Spectral decomposition using Fourier transform and wavelets
  • Deep autoencoders for time series clustering
  • Linear and nonlinear models for complex physiological systems’ dynamics
(iii) RNA-seq data analysis
  • Enrichment analysis
  • Dimension reduction (UMAP, PCA, t-SNE)
(iv) Model calibration 
  • Global optimisation heuristics (particle swarm, genetic algorithms)
  • Probabilistic and Bayesian methods. 
 
 
 
LEARNING AND TEACHING
LEARNING ACTIVITIES AND TEACHING METHODS (given in hours of study time)
Scheduled Learning & Teaching Activities 33 Guided Independent Study 117 Placement / Study Abroad 0
DETAILS OF LEARNING ACTIVITIES AND TEACHING METHODS
Category Hours of study time Description
Scheduled learning and teaching activities 11 In lectures, problems and data are introduced; background theory is described
Scheduled learning and teaching activities 22
Students use the knowledge from the lecture to perform a hands-on data analysis task with real data
Guided Independent Study 117
Independent reading and problem solving

 

ASSESSMENT
FORMATIVE ASSESSMENT - for feedback and development purposes; does not count towards module grade
Form of Assessment Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Questions in practical sessions 11 x 3 hours 1-11 Oral, in class

 

SUMMATIVE ASSESSMENT (% of credit)
Coursework 100 Written Exams 0 Practical Exams 0
DETAILS OF SUMMATIVE ASSESSMENT
Form of Assessment % of Credit Size of Assessment (e.g. duration/length) ILOs Assessed Feedback Method
Coursework 1 50 4 weeks to complete 1-11 Marked script
Coursework 2 50 4 weeks to complete 1-11 Marked script

 

DETAILS OF RE-ASSESSMENT (where required by referral or deferral)
Original Form of Assessment Form of Re-assessment ILOs Re-assessed Time Scale for Re-assessment
Coursework 1 Coursework piece (50%) 1-11 Referral/Deferral period
Coursework 2 Coursework piece (50%) 1-11 Referral/Deferral period

 

RE-ASSESSMENT NOTES

Deferral – if you have been deferred for any assessment you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 40%) you will be expected to submit the relevant assessment. The mark given for a re-assessment taken as a result of referral will be capped at 40%.

RESOURCES
INDICATIVE LEARNING RESOURCES - The following list is offered as an indication of the type & level of
information that you are expected to consult. Further guidance will be provided by the Module Convener

Reading list for this module:

Type Author Title Edition Publisher Year ISBN
Reference Cohen, M.X. Analyzing Neural Time Series Data: Theory and Practice
Reference Eberhart, R. Shui, Y. and Kennedy, J. Swarm Intelligence
Reference Lee, J.A. , Verleysen, M. and Schölkopf, B. Nonlinear Dimensionality Reduction
CREDIT VALUE 15 ECTS VALUE 7.5
PRE-REQUISITE MODULES None
CO-REQUISITE MODULES None
NQF LEVEL (FHEQ) 7 AVAILABLE AS DISTANCE LEARNING No
ORIGIN DATE Tuesday 17th January 2023 LAST REVISION DATE Thursday 7th March 2024
KEY WORDS SEARCH Data analytics, Biomedical data, Health data, Model calibration, AI, Time series analysis, Modelling, Image analysis

Please note that all modules are subject to change, please get in touch if you have any questions about this module.